Key Results

Developed a new computer vision algorithm that detects moving objects in public spaces and then calculates the percentages of people using either stairs or escalators.

Obtained approval from the Metropolitan Boston Transit Authority to test the new technology and conduct just-in-time messaging experiments in three Boston commuter rail stations.

Key Findings

The new people-counting technology accurately calculated the percentage of stair users versus escalator users, adapted well to the difficult environment of the transit stations and was cost-effective.

When researchers used their new technology to project a motivational message ("Your heart needs exercise, here's your chance") in the transit stations, commuters increased their stair use by 4.3 percent, a finding consistent with prior studies.